{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('toutiao_cat_data.txt', 'r', encoding='utf8')\n",
    "text = []\n",
    "for line in f:\n",
    "    line = line.split('_!_')\n",
    "    text.append(line[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "news = pd.DataFrame(np.array(text)).loc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382688\n"
     ]
    }
   ],
   "source": [
    "from pyhanlp import *\n",
    "seg_list_all = []\n",
    "for i in news[2]:\n",
    "    seg_list = HanLP.segment(i)\n",
    "    word_list = []\n",
    "    for word_ in seg_list:\n",
    "        word_list.append(word_.word)\n",
    "    word_str = ' '.join(word_list)\n",
    "    seg_list_all.append(word_str)\n",
    "    seg_list_all.append(len(word_list))\n",
    "#seg_list_all\n",
    "news = pd.DataFrame(np.array(text)).loc[:,:2]\n",
    "news.columns = ['label_num','label_name','content']\n",
    "print(len(news))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seg = pd.DataFrame(np.array(seg_list_all).reshape(382688,2))\n",
    "df_seg.columns = ['seg_word','word_count']\n",
    "news_all = pd.concat([news,df_seg],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_num</th>\n",
       "      <th>label_name</th>\n",
       "      <th>content</th>\n",
       "      <th>seg_word</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>news_culture</td>\n",
       "      <td>京城最值得你来场文化之旅的博物馆</td>\n",
       "      <td>京城 最 值得 你 来 场 文化 之旅 的 博物馆</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>news_culture</td>\n",
       "      <td>发酵床的垫料种类有哪些？哪种更好？</td>\n",
       "      <td>发酵 床 的 垫料 种类 有 哪些 ？ 哪种 更好 ？</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>news_culture</td>\n",
       "      <td>上联：黄山黄河黄皮肤黄土高原。怎么对下联？</td>\n",
       "      <td>上联 ： 黄山 黄河 黄皮肤 黄土高原 。 怎么 对 下联 ？</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>news_culture</td>\n",
       "      <td>林徽因什么理由拒绝了徐志摩而选择梁思成为终身伴侣？</td>\n",
       "      <td>林徽因 什么 理由 拒绝 了 徐志摩 而 选择 梁思成 为 终身伴侣 ？</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>news_culture</td>\n",
       "      <td>黄杨木是什么树？</td>\n",
       "      <td>黄杨 木 是 什么 树 ？</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label_num    label_name                    content  \\\n",
       "0       101  news_culture           京城最值得你来场文化之旅的博物馆   \n",
       "1       101  news_culture          发酵床的垫料种类有哪些？哪种更好？   \n",
       "2       101  news_culture      上联：黄山黄河黄皮肤黄土高原。怎么对下联？   \n",
       "3       101  news_culture  林徽因什么理由拒绝了徐志摩而选择梁思成为终身伴侣？   \n",
       "4       101  news_culture                   黄杨木是什么树？   \n",
       "\n",
       "                               seg_word word_count  \n",
       "0             京城 最 值得 你 来 场 文化 之旅 的 博物馆         10  \n",
       "1           发酵 床 的 垫料 种类 有 哪些 ？ 哪种 更好 ？         11  \n",
       "2       上联 ： 黄山 黄河 黄皮肤 黄土高原 。 怎么 对 下联 ？         11  \n",
       "3  林徽因 什么 理由 拒绝 了 徐志摩 而 选择 梁思成 为 终身伴侣 ？         12  \n",
       "4                         黄杨 木 是 什么 树 ？          6  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rate = 0.4\n",
    "val_rate = 0.4\n",
    "test_rate =0.2\n",
    "train_df = news_all.iloc[:int(train_rate*len(news))]\n",
    "val_df = news_all.iloc[int(train_rate*len(news)):int((train_rate+val_rate)*len(news))]\n",
    "test_df = news_all.iloc[int((train_rate+val_rate)*len(news)):]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_num</th>\n",
       "      <th>label_name</th>\n",
       "      <th>content</th>\n",
       "      <th>seg_word</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153075</th>\n",
       "      <td>110</td>\n",
       "      <td>news_military</td>\n",
       "      <td>哈萨克斯坦总统纳扎尔巴耶夫在江布尔州阅兵</td>\n",
       "      <td>哈萨克斯坦 总统 纳扎尔巴耶夫 在 江布尔州 阅兵</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153076</th>\n",
       "      <td>112</td>\n",
       "      <td>news_travel</td>\n",
       "      <td>北京西城区整治什刹海管理辖区旅游大巴乱象</td>\n",
       "      <td>北京 西城区 整治 什刹海 管理 辖区 旅游 大巴 乱象</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153077</th>\n",
       "      <td>112</td>\n",
       "      <td>news_travel</td>\n",
       "      <td>刀剑如画：盘点十处武侠片里的美丽景色</td>\n",
       "      <td>刀剑 如 画 ： 盘点 十 处 武侠片 里 的 美丽 景色</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153078</th>\n",
       "      <td>112</td>\n",
       "      <td>news_travel</td>\n",
       "      <td>后来的我们 牵手去阳朔</td>\n",
       "      <td>后来 的 我们   牵手 去 阳朔</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153079</th>\n",
       "      <td>112</td>\n",
       "      <td>news_travel</td>\n",
       "      <td>这些地方动物泛滥成灾，已经威胁到了人类的生存</td>\n",
       "      <td>这些 地方 动物 泛滥成灾 ， 已经 威胁 到 了 人类 的 生存</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       label_num     label_name                 content  \\\n",
       "153075       110  news_military    哈萨克斯坦总统纳扎尔巴耶夫在江布尔州阅兵   \n",
       "153076       112    news_travel    北京西城区整治什刹海管理辖区旅游大巴乱象   \n",
       "153077       112    news_travel      刀剑如画：盘点十处武侠片里的美丽景色   \n",
       "153078       112    news_travel             后来的我们 牵手去阳朔   \n",
       "153079       112    news_travel  这些地方动物泛滥成灾，已经威胁到了人类的生存   \n",
       "\n",
       "                                 seg_word word_count  \n",
       "153075          哈萨克斯坦 总统 纳扎尔巴耶夫 在 江布尔州 阅兵          6  \n",
       "153076       北京 西城区 整治 什刹海 管理 辖区 旅游 大巴 乱象          9  \n",
       "153077      刀剑 如 画 ： 盘点 十 处 武侠片 里 的 美丽 景色         12  \n",
       "153078                  后来 的 我们   牵手 去 阳朔          7  \n",
       "153079  这些 地方 动物 泛滥成灾 ， 已经 威胁 到 了 人类 的 生存         12  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_num</th>\n",
       "      <th>label_name</th>\n",
       "      <th>content</th>\n",
       "      <th>seg_word</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>306150</th>\n",
       "      <td>109</td>\n",
       "      <td>news_tech</td>\n",
       "      <td>马云演讲 人工智能到来后，你每天只需工作4小时</td>\n",
       "      <td>马云 演讲   人工智能 到来 后 ， 你 每天 只需 工作 4 小时</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306151</th>\n",
       "      <td>110</td>\n",
       "      <td>news_military</td>\n",
       "      <td>太霸道了：任何有美国零件的卫星都不能交给我国火箭发射</td>\n",
       "      <td>太 霸道 了 ： 任何 有 美国 零件 的 卫星 都 不能 交给 我国 火箭 发射</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306152</th>\n",
       "      <td>110</td>\n",
       "      <td>news_military</td>\n",
       "      <td>2018年女兵征集时间已确定，只有短短42天，今年报名不一定能体检</td>\n",
       "      <td>2018 年 女兵 征集 时间 已确定 ， 只有 短短 42 天 ， 今年 报名 不一定 能 体检</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306153</th>\n",
       "      <td>115</td>\n",
       "      <td>news_agriculture</td>\n",
       "      <td>韭菜播种难？专家有高招！</td>\n",
       "      <td>韭菜 播种 难 ？ 专家 有 高招 ！</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306154</th>\n",
       "      <td>116</td>\n",
       "      <td>news_game</td>\n",
       "      <td>《绝地求生》：98再也不是神，只有空头才是永远的梦想</td>\n",
       "      <td>《 绝地 求生 》 ： 98 再也不是 神 ， 只有 空头 才 是 永远 的 梦想</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       label_num        label_name                            content  \\\n",
       "306150       109         news_tech            马云演讲 人工智能到来后，你每天只需工作4小时   \n",
       "306151       110     news_military         太霸道了：任何有美国零件的卫星都不能交给我国火箭发射   \n",
       "306152       110     news_military  2018年女兵征集时间已确定，只有短短42天，今年报名不一定能体检   \n",
       "306153       115  news_agriculture                       韭菜播种难？专家有高招！   \n",
       "306154       116         news_game         《绝地求生》：98再也不是神，只有空头才是永远的梦想   \n",
       "\n",
       "                                                 seg_word word_count  \n",
       "306150                马云 演讲   人工智能 到来 后 ， 你 每天 只需 工作 4 小时         13  \n",
       "306151          太 霸道 了 ： 任何 有 美国 零件 的 卫星 都 不能 交给 我国 火箭 发射         16  \n",
       "306152  2018 年 女兵 征集 时间 已确定 ， 只有 短短 42 天 ， 今年 报名 不一定 能 体检         17  \n",
       "306153                                韭菜 播种 难 ？ 专家 有 高招 ！          8  \n",
       "306154          《 绝地 求生 》 ： 98 再也不是 神 ， 只有 空头 才 是 永远 的 梦想         16  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9fb1e7ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "sns.countplot(train_df.label_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     153075\n",
      "unique        44\n",
      "top           11\n",
      "freq       11210\n",
      "Name: word_count, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(train_df.word_count.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 對dataset label 做編碼  ，  LabelEncoder可以將label分配成0~classes-1的編碼\n",
    "train_y = train_df.label_name\n",
    "val_y = val_df.label_name\n",
    "test_y = test_df.label_name\n",
    "\n",
    "le = LabelEncoder()\n",
    "train_y = le.fit_transform(train_y).reshape(-1,1)\n",
    "val_y = le.transform(val_y).reshape(-1,1)\n",
    "test_y = le.transform(test_y).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2],\n",
       "       [ 2],\n",
       "       [ 2],\n",
       "       ...,\n",
       "       [11],\n",
       "       [11],\n",
       "       [ 8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:414: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "## 對dataset label做one-hot encoding\n",
    "ohe = OneHotEncoder()\n",
    "train_y = ohe.fit_transform(train_y).toarray()\n",
    "val_y = ohe.transform(val_y).toarray()\n",
    "test_y = ohe.transform(test_y).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('，', 1)\n",
      "('？', 2)\n",
      "('的', 3)\n",
      "('！', 4)\n",
      "('：', 5)\n",
      "('了', 6)\n",
      "('是', 7)\n",
      "('你', 8)\n",
      "('“', 9)\n",
      "('”', 10)\n",
      "===================\n",
      "('京城', 14)\n",
      "('最', 5000)\n",
      "('值得', 950)\n",
      "('你', 14997)\n",
      "('来', 2568)\n",
      "('场', 728)\n",
      "('文化', 472)\n",
      "('之旅', 139)\n",
      "('的', 60803)\n",
      "('博物馆', 112)\n"
     ]
    }
   ],
   "source": [
    "# Tokenizer可以對文本的詞進行統計次數，生成文檔詞典，以支持基於詞典位序生成文本的向量表示\n",
    "\n",
    "## 使用Tokenizer對詞組進行編碼\n",
    "## 當我們建立一個Tokenizer對象後，使用該對象的fit_on_texts()函数，以空格去辨識每個詞,\n",
    "## 可以將輸入的文本中的每個詞編號，編號是根據詞頻的，詞頻越大，編號越小。\n",
    "\n",
    "max_words = 5000\n",
    "max_len = 600\n",
    "tok = Tokenizer(num_words=max_words)  ## 使用的最大詞語數为5000\n",
    "tok.fit_on_texts(train_df.seg_word)## 使用word_index屬性可以看到每個詞對應的編碼\n",
    "## 使用word_counts屬性可以看到每個詞對應的頻數\n",
    "for ii,iterm in enumerate(tok.word_index.items()):\n",
    "    if ii < 10:\n",
    "        print(iterm)\n",
    "    else:\n",
    "        break\n",
    "print(\"===================\")  \n",
    "for ii,iterm in enumerate(tok.word_counts.items()):\n",
    "    if ii < 10:\n",
    "        print(iterm)\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 將切割的詞轉成數字\n",
    "train_seq = tok.texts_to_sequences(train_df.seg_word)\n",
    "val_seq = tok.texts_to_sequences(val_df.seg_word)\n",
    "test_seq = tok.texts_to_sequences(test_df.seg_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  2],\n",
       " [4123, 52, 95, 2372, 159, 223, 88, 365, 56, 347, 2824],\n",
       " [704, 123, 191, 1, 193, 2688, 2297, 11, 48, 2176, 2],\n",
       " [17, 2073, 174, 377, 159, 26, 121, 2636, 2],\n",
       " [22, 24, 36, 3, 107],\n",
       " [1024,\n",
       "  238,\n",
       "  1487,\n",
       "  5,\n",
       "  2255,\n",
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       "  240,\n",
       "  3208,\n",
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       "  1,\n",
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       "  35,\n",
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       " [110, 2298, 623, 2688, 47, 465, 2, 53, 97, 69, 1247, 4342, 6, 12, 2],\n",
       " ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153075, 600)\n",
      "(153075, 600)\n",
      "(76538, 600)\n"
     ]
    }
   ],
   "source": [
    "## 將每個序列調整相同的長度\n",
    "train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)\n",
    "val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)\n",
    "test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)\n",
    "print(train_seq_mat.shape)\n",
    "print(val_seq_mat.shape)\n",
    "print(test_seq_mat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0, ..., 1857,    3, 2280],\n",
       "       [   0,    0,    0, ..., 1830,  792,    2],\n",
       "       [   0,    0,    0, ...,   32,   50,    2],\n",
       "       ...,\n",
       "       [   0,    0,    0, ...,   78, 4222,  647],\n",
       "       [   0,    0,    0, ...,  726,  345, 2735],\n",
       "       [   0,    0,    0, ...,    0,    0, 1009]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_seq_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0730 15:20:48.959594 22696 deprecation.py:323] From D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs (InputLayer)          [(None, 600)]             0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 600, 128)          640128    \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 128)               131584    \n",
      "_________________________________________________________________\n",
      "FC1 (Dense)                  (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "FC2 (Dense)                  (None, 15)                1935      \n",
      "=================================================================\n",
      "Total params: 790,159\n",
      "Trainable params: 790,159\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 153075 samples, validate on 153075 samples\n",
      "Epoch 1/10\n",
      "152960/153075 [============================>.] - ETA: 3s - loss: 1.2065 - accuracy: 0.6395"
     ]
    }
   ],
   "source": [
    "## LSTM模型\n",
    "inputs = Input(name='inputs',shape=[max_len])\n",
    "## Embedding(詞彙表大小,batch大小,每個新聞的詞長)\n",
    "layer = Embedding(max_words+1,128,input_length=max_len)(inputs)\n",
    "layer = LSTM(128)(layer)\n",
    "layer = Dense(128,activation=\"relu\",name=\"FC1\")(layer)\n",
    "layer = Dropout(0.5)(layer)\n",
    "layer = Dense(15,activation=\"softmax\",name=\"FC2\")(layer)\n",
    "model = Model(inputs=inputs,outputs=layer)\n",
    "model.summary()\n",
    "model.compile(loss=\"categorical_crossentropy\",optimizer=RMSprop(),metrics=[\"accuracy\"])\n",
    "# In[]\n",
    "model_fit = model.fit(train_seq_mat,train_y,batch_size=128,epochs=10,\n",
    "                      validation_data=(val_seq_mat,val_y),\n",
    "                      callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)] ## 當val-loss不再提升時停止訓練\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pre = model.predict(test_seq_mat)\n",
    "## 評價預測效果，計算混淆矩陣\n",
    "confm = metrics.confusion_matrix(np.argmax(test_pre,axis=1),np.argmax(test_y,axis=1))\n",
    "## 混淆矩陣可視化\n",
    "Labname = [\"体育\",\"娱乐\",\"家居\",\"房产\",\"教育\",\"时尚\",\"时政\",\"游戏\",\"科技\",\"财经\"]\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(confm.T, square=True, annot=True,\n",
    "            fmt='d', cbar=False,linewidths=.8,\n",
    "            cmap=\"YlGnBu\")\n",
    "plt.xlabel('True label',size = 14)\n",
    "plt.ylabel('Predicted label',size = 14)\n",
    "plt.xticks(np.arange(10)+0.5,Labname,fontproperties = fonts,size = 12)\n",
    "plt.yticks(np.arange(10)+0.3,Labname,fontproperties = fonts,size = 12)\n",
    "plt.show()\n",
    "print(metrics.classification_report(np.argmax(test_pre,axis=1),np.argmax(test_y,axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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